“If you can’t explain it simply, you don’t understand it well enough.” - Albert Einstein

1) Out-of-the-Box Ai Ready - The Ai Verticalization

In the future, will it be better if you know how to train a Machine Learning (ML) algorithm or, become an expert that understands the out-of-the-box Ai vendor landscape for your industry, buy vs. build tradeoff and Ai solution vendor vetting process? Here are three good articles and the related section #2 below.

New full stack vertical AI startups are popping up in financial services, life sciences and healthcare, energy, transportation, heavy industry, agriculture, and materials. These startups will solve high level domain problems powered by proprietary data and machine learning models.

Let’s continue to explore by further differentiating ML algorithms into three groups:1) Supervised (samples of result dataset initially trains algorithm)2) Unsupervised (algorithm isn’t pre-trained, learns as it “reads” data)3) Reinforcement Learning (algorithm plays game for rewards to improve)

Below, see a very high-level ML landscape diagram. As you can see, there are a mind-boggling number of ML algorithms, does not include Reinforcement Learning and more being developed at an increasing rate. To program those ML algorithms, you have to configure them and train them with data to build an intelligent system.

Machine Learning Mindmap from MachineLearningMastery.com

My point is that you do not need to learn how to configure and train ML models to make them work for you in your business any more than you have to learn the underlying software code that makes, for example, Microsoft Word or Excel work for you.

Even if you want to work directly with ML algorithms and build intelligent systems from “scratch,” that building process is itself being automated. Yes, by other ML algorithms (Reinforcement Learning) that optimize the building process automatically. See cutting-edge DataRobot Machine Learning Automation software company website below.

Google revealed a major new approach to A.I. development that seems to call out to the most sensational and apocalyptic predictions in all of science fiction. Called “AutoML” for “auto-machine learning,” it allows one A.I. to become the architect of another, and direct its development without the need for input from a human engineer.

The decision becomes which approach can help you make the best business case either for hiring a machine learning expert or undertaking evaluating out-of-the-box Ai solution vendors. Somewhere in between the build vs. buy continuum is deciding to us pre-trained Machine Learning APIs from, for example, Google, AWS, IBM, Microsoft for sentiment analysis, image recognition, job search, e.g., Google Cloud AI

I want to talk about a mistake I see client after client making…. There is a myth, of course, a myth that grows with nigh every case study at every MBA school, that somewhere within the analytics data your site or app or service collects, in some obscure row or column, you will find the secret to your ultimate success.

I manage a group of machine learning and data scientist professionals at Everymans.ai, a boutique marketing, and Ai enabling consultancy. We help companies quickly exploit marketing opportunities by building AI-enabled Minimum Viable Products (MVP) that will differentiate their products and services to gain a competitive edge with Ai.

Have an early stage Ai startup looking for advice, investment or want to explore your MVP ideas, I’d love to hear from you.